Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical3

Alerts

race is highly imbalanced (72.1%)Imbalance
age has unique valuesUnique
capital-gain has unique valuesUnique
workclass has 817 (8.2%) zerosZeros
education has 255 (2.5%) zerosZeros
marital-status has 1478 (14.8%) zerosZeros
occupation has 1276 (12.8%) zerosZeros
relationship has 4620 (46.2%) zerosZeros
native-country has 134 (1.3%) zerosZeros

Reproduction

Analysis started2023-12-07 22:54:39.896915
Analysis finished2023-12-07 22:54:55.275328
Duration15.38 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.80156
Minimum13.253723
Maximum105.02939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:55.333101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13.253723
5-th percentile17.692909
Q126.480108
median34.329525
Q342.801784
95-th percentile60.73332
Maximum105.02939
Range91.775667
Interquartile range (IQR)16.321676

Descriptive statistics

Standard deviation13.234366
Coefficient of variation (CV)0.36965893
Kurtosis1.5652641
Mean35.80156
Median Absolute Deviation (MAD)8.1143218
Skewness1.0068311
Sum358015.6
Variance175.14845
MonotonicityNot monotonic
2023-12-07T22:54:55.448199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.42623395 1
 
< 0.1%
33.47605882 1
 
< 0.1%
51.49675285 1
 
< 0.1%
52.34532394 1
 
< 0.1%
31.05434401 1
 
< 0.1%
15.03968694 1
 
< 0.1%
18.89680623 1
 
< 0.1%
33.4505458 1
 
< 0.1%
34.1117228 1
 
< 0.1%
16.33749839 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
13.253723 1
< 0.1%
13.31528595 1
< 0.1%
13.61147563 1
< 0.1%
13.63157115 1
< 0.1%
13.67428287 1
< 0.1%
13.7243673 1
< 0.1%
13.77827064 1
< 0.1%
13.7895904 1
< 0.1%
13.79679007 1
< 0.1%
13.7999897 1
< 0.1%
ValueCountFrequency (%)
105.0293905 1
< 0.1%
102.1843329 1
< 0.1%
99.80568432 1
< 0.1%
96.31696959 1
< 0.1%
94.26557406 1
< 0.1%
93.88988886 1
< 0.1%
93.38837156 1
< 0.1%
92.64809681 1
< 0.1%
91.80377173 1
< 0.1%
91.41977369 1
< 0.1%

workclass
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6691
Minimum0
Maximum8
Zeros817
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:55.542900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6738236
Coefficient of variation (CV)0.45619459
Kurtosis0.32634182
Mean3.6691
Median Absolute Deviation (MAD)0
Skewness-0.5695488
Sum36691
Variance2.8016854
MonotonicityNot monotonic
2023-12-07T22:54:55.626107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 6042
60.4%
2 934
 
9.3%
6 910
 
9.1%
0 817
 
8.2%
1 514
 
5.1%
7 384
 
3.8%
5 368
 
3.7%
3 19
 
0.2%
8 12
 
0.1%
ValueCountFrequency (%)
0 817
 
8.2%
1 514
 
5.1%
2 934
 
9.3%
3 19
 
0.2%
4 6042
60.4%
5 368
 
3.7%
6 910
 
9.1%
7 384
 
3.8%
8 12
 
0.1%
ValueCountFrequency (%)
8 12
 
0.1%
7 384
 
3.8%
6 910
 
9.1%
5 368
 
3.7%
4 6042
60.4%
3 19
 
0.2%
2 934
 
9.3%
1 514
 
5.1%
0 817
 
8.2%

fnlwgt
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190645.2
Minimum-4265.5312
Maximum1152958.3
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)0.1%
Memory size78.2 KiB
2023-12-07T22:54:55.737628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4265.5312
5-th percentile42648.068
Q1123351.4
median178107.7
Q3235137.46
95-th percentile399321.48
Maximum1152958.3
Range1157223.8
Interquartile range (IQR)111786.06

Descriptive statistics

Standard deviation106585.54
Coefficient of variation (CV)0.55907802
Kurtosis5.1603199
Mean190645.2
Median Absolute Deviation (MAD)55963.151
Skewness1.429964
Sum1.906452 × 109
Variance1.1360478 × 1010
MonotonicityNot monotonic
2023-12-07T22:54:55.859573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237043.9545 2
 
< 0.1%
33512.99359 1
 
< 0.1%
330847.5676 1
 
< 0.1%
225040.2482 1
 
< 0.1%
306611.0177 1
 
< 0.1%
218952.3342 1
 
< 0.1%
203691.7827 1
 
< 0.1%
128932.4123 1
 
< 0.1%
30593.14703 1
 
< 0.1%
241476.6135 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
-4265.531207 1
< 0.1%
-3634.933404 1
< 0.1%
-2370.857371 1
< 0.1%
-1769.417418 1
< 0.1%
-1755.846067 1
< 0.1%
-1286.695882 1
< 0.1%
-913.9393712 1
< 0.1%
-623.3555064 1
< 0.1%
245.6076511 1
< 0.1%
1275.659092 1
< 0.1%
ValueCountFrequency (%)
1152958.258 1
< 0.1%
1087774.398 1
< 0.1%
1032063.775 1
< 0.1%
999532.3943 1
< 0.1%
978093.5148 1
< 0.1%
959298.0064 1
< 0.1%
941990.6443 1
< 0.1%
941153.9102 1
< 0.1%
910806.9286 1
< 0.1%
881025.7012 1
< 0.1%

education
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8822
Minimum0
Maximum15
Zeros255
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:55.964445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median11
Q311
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8729089
Coefficient of variation (CV)0.39190756
Kurtosis0.50351297
Mean9.8822
Median Absolute Deviation (MAD)2
Skewness-0.93598383
Sum98822
Variance14.999423
MonotonicityNot monotonic
2023-12-07T22:54:56.046123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 3890
38.9%
15 1587
15.9%
9 1160
 
11.6%
1 485
 
4.9%
12 459
 
4.6%
8 457
 
4.6%
7 386
 
3.9%
0 255
 
2.5%
10 253
 
2.5%
5 233
 
2.3%
Other values (6) 835
 
8.3%
ValueCountFrequency (%)
0 255
 
2.5%
1 485
4.9%
2 159
 
1.6%
3 109
 
1.1%
4 171
 
1.7%
5 233
 
2.3%
6 135
 
1.4%
7 386
 
3.9%
8 457
 
4.6%
9 1160
11.6%
ValueCountFrequency (%)
15 1587
15.9%
14 209
 
2.1%
13 52
 
0.5%
12 459
 
4.6%
11 3890
38.9%
10 253
 
2.5%
9 1160
 
11.6%
8 457
 
4.6%
7 386
 
3.9%
6 135
 
1.4%

education-num
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8858
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:56.136138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median10
Q312
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0730283
Coefficient of variation (CV)0.31085276
Kurtosis0.14830832
Mean9.8858
Median Absolute Deviation (MAD)1
Skewness-0.45669183
Sum98858
Variance9.4435027
MonotonicityNot monotonic
2023-12-07T22:54:56.216558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 2494
24.9%
10 2328
23.3%
14 1111
11.1%
13 960
 
9.6%
12 457
 
4.6%
4 430
 
4.3%
11 428
 
4.3%
7 385
 
3.9%
6 273
 
2.7%
5 266
 
2.7%
Other values (6) 868
 
8.7%
ValueCountFrequency (%)
1 49
 
0.5%
2 132
 
1.3%
3 194
 
1.9%
4 430
 
4.3%
5 266
 
2.7%
6 273
 
2.7%
7 385
 
3.9%
8 136
 
1.4%
9 2494
24.9%
10 2328
23.3%
ValueCountFrequency (%)
16 185
 
1.8%
15 172
 
1.7%
14 1111
11.1%
13 960
 
9.6%
12 457
 
4.6%
11 428
 
4.3%
10 2328
23.3%
9 2494
24.9%
8 136
 
1.4%
7 385
 
3.9%

marital-status
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6315
Minimum0
Maximum6
Zeros1478
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:56.534777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5416048
Coefficient of variation (CV)0.5858274
Kurtosis-0.61040044
Mean2.6315
Median Absolute Deviation (MAD)2
Skewness-0.079193128
Sum26315
Variance2.3765454
MonotonicityNot monotonic
2023-12-07T22:54:56.606317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 4222
42.2%
4 3449
34.5%
0 1478
 
14.8%
6 328
 
3.3%
5 284
 
2.8%
3 224
 
2.2%
1 15
 
0.1%
ValueCountFrequency (%)
0 1478
 
14.8%
1 15
 
0.1%
2 4222
42.2%
3 224
 
2.2%
4 3449
34.5%
5 284
 
2.8%
6 328
 
3.3%
ValueCountFrequency (%)
6 328
 
3.3%
5 284
 
2.8%
4 3449
34.5%
3 224
 
2.2%
2 4222
42.2%
1 15
 
0.1%
0 1478
 
14.8%

occupation
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9165
Minimum0
Maximum14
Zeros1276
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:56.689573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2955993
Coefficient of variation (CV)0.72603723
Kurtosis-1.2014529
Mean5.9165
Median Absolute Deviation (MAD)4
Skewness0.22249408
Sum59165
Variance18.452173
MonotonicityNot monotonic
2023-12-07T22:54:56.770871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 1867
18.7%
0 1276
12.8%
8 1120
11.2%
10 1119
11.2%
12 986
9.9%
1 888
8.9%
7 644
 
6.4%
4 619
 
6.2%
14 386
 
3.9%
5 373
 
3.7%
Other values (5) 722
 
7.2%
ValueCountFrequency (%)
0 1276
12.8%
1 888
8.9%
2 25
 
0.2%
3 1867
18.7%
4 619
 
6.2%
5 373
 
3.7%
6 300
 
3.0%
7 644
 
6.4%
8 1120
11.2%
9 68
 
0.7%
ValueCountFrequency (%)
14 386
 
3.9%
13 180
 
1.8%
12 986
9.9%
11 149
 
1.5%
10 1119
11.2%
9 68
 
0.7%
8 1120
11.2%
7 644
6.4%
6 300
 
3.0%
5 373
 
3.7%

relationship
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4924
Minimum0
Maximum5
Zeros4620
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:56.858673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6974777
Coefficient of variation (CV)1.1374147
Kurtosis-1.0521511
Mean1.4924
Median Absolute Deviation (MAD)1
Skewness0.6683013
Sum14924
Variance2.8814304
MonotonicityNot monotonic
2023-12-07T22:54:56.936228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4620
46.2%
3 1672
 
16.7%
1 1537
 
15.4%
4 1194
 
11.9%
5 547
 
5.5%
2 430
 
4.3%
ValueCountFrequency (%)
0 4620
46.2%
1 1537
 
15.4%
2 430
 
4.3%
3 1672
 
16.7%
4 1194
 
11.9%
5 547
 
5.5%
ValueCountFrequency (%)
5 547
 
5.5%
4 1194
 
11.9%
3 1672
 
16.7%
2 430
 
4.3%
1 1537
 
15.4%
0 4620
46.2%

race
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
4
8936 
2
 
623
1
 
297
3
 
87
0
 
57

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

Length

2023-12-07T22:54:57.020657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:54:57.113091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

Most occurring characters

ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 8936
89.4%
2 623
 
6.2%
1 297
 
3.0%
3 87
 
0.9%
0 57
 
0.6%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5955 
0
4045 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

Length

2023-12-07T22:54:57.192711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:54:57.278399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

Most occurring characters

ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5955
59.6%
0 4045
40.5%

capital-gain
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2682.5949
Minimum-1913.2347
Maximum123074.37
Zeros0
Zeros (%)0.0%
Negative4638
Negative (%)46.4%
Memory size78.2 KiB
2023-12-07T22:54:57.366670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1913.2347
5-th percentile-113.97831
Q1-56.686392
median10.166255
Q394.483273
95-th percentile13169.569
Maximum123074.37
Range124987.6
Interquartile range (IQR)151.16967

Descriptive statistics

Standard deviation11806.093
Coefficient of variation (CV)4.4009973
Kurtosis60.398078
Mean2682.5949
Median Absolute Deviation (MAD)73.451276
Skewness7.527322
Sum26825949
Variance1.3938383 × 108
MonotonicityNot monotonic
2023-12-07T22:54:57.493468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.71298691 1
 
< 0.1%
114.9299012 1
 
< 0.1%
842.9985567 1
 
< 0.1%
101.7188548 1
 
< 0.1%
99.33068808 1
 
< 0.1%
39.69315116 1
 
< 0.1%
-80.51026183 1
 
< 0.1%
84277.1112 1
 
< 0.1%
67.51765831 1
 
< 0.1%
12290.59791 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-1913.234698 1
< 0.1%
-1854.802914 1
< 0.1%
-1822.593837 1
< 0.1%
-1611.872358 1
< 0.1%
-1497.689087 1
< 0.1%
-1473.581214 1
< 0.1%
-1435.438832 1
< 0.1%
-1429.901932 1
< 0.1%
-1403.61311 1
< 0.1%
-1238.173059 1
< 0.1%
ValueCountFrequency (%)
123074.3699 1
< 0.1%
122920.4687 1
< 0.1%
121824.594 1
< 0.1%
121372.3405 1
< 0.1%
120794.2426 1
< 0.1%
119942.4656 1
< 0.1%
119804.6572 1
< 0.1%
119459.6174 1
< 0.1%
118995.5597 1
< 0.1%
118528.1543 1
< 0.1%

capital-loss
Real number (ℝ)

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.701187
Minimum-7.4920681
Maximum3111.6087
Zeros0
Zeros (%)0.0%
Negative6610
Negative (%)66.1%
Memory size78.2 KiB
2023-12-07T22:54:57.618049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-7.4920681
5-th percentile-6.4831587
Q1-4.5091744
median-1.9732618
Q31.2530795
95-th percentile5.6527159
Maximum3111.6087
Range3119.1007
Interquartile range (IQR)5.7622538

Descriptive statistics

Standard deviation303.11376
Coefficient of variation (CV)5.9784352
Kurtosis35.10283
Mean50.701187
Median Absolute Deviation (MAD)2.8224199
Skewness5.9180979
Sum507011.87
Variance91877.951
MonotonicityNot monotonic
2023-12-07T22:54:57.739536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.344715468 2
 
< 0.1%
4.514324008 2
 
< 0.1%
0.6323548472 2
 
< 0.1%
-3.549109534 1
 
< 0.1%
1.597165456 1
 
< 0.1%
-5.098919567 1
 
< 0.1%
5.927915521 1
 
< 0.1%
-6.904283028 1
 
< 0.1%
-2.679940538 1
 
< 0.1%
-6.219695965 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
-7.492068079 1
< 0.1%
-7.486674418 1
< 0.1%
-7.46900707 1
< 0.1%
-7.466165568 1
< 0.1%
-7.461362444 1
< 0.1%
-7.460763249 1
< 0.1%
-7.45493261 1
< 0.1%
-7.453376983 1
< 0.1%
-7.443305506 1
< 0.1%
-7.427548289 1
< 0.1%
ValueCountFrequency (%)
3111.608651 1
< 0.1%
3094.773145 1
< 0.1%
3055.782902 1
< 0.1%
2867.454631 1
< 0.1%
2790.67168 1
< 0.1%
2720.418574 1
< 0.1%
2673.17814 1
< 0.1%
2661.757182 1
< 0.1%
2649.753209 1
< 0.1%
2597.935411 1
< 0.1%

hours-per-week
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.31361
Minimum-7.7514182
Maximum117.37886
Zeros0
Zeros (%)0.0%
Negative151
Negative (%)1.5%
Memory size78.2 KiB
2023-12-07T22:54:57.858984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-7.7514182
5-th percentile12.550952
Q139.74206
median39.926922
Q340.154293
95-th percentile55.982556
Maximum117.37886
Range125.13027
Interquartile range (IQR)0.4122338

Descriptive statistics

Standard deviation11.374256
Coefficient of variation (CV)0.29687248
Kurtosis5.9102673
Mean38.31361
Median Absolute Deviation (MAD)0.20142734
Skewness-0.80484202
Sum383136.1
Variance129.37371
MonotonicityNot monotonic
2023-12-07T22:54:57.977096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.8181023 2
 
< 0.1%
39.97167261 1
 
< 0.1%
35.41662975 1
 
< 0.1%
39.8712364 1
 
< 0.1%
39.93698594 1
 
< 0.1%
22.67485279 1
 
< 0.1%
39.8326533 1
 
< 0.1%
21.06893913 1
 
< 0.1%
40.07141854 1
 
< 0.1%
40.03405905 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
-7.75141821 1
< 0.1%
-7.68790913 1
< 0.1%
-7.549939844 1
< 0.1%
-7.491037189 1
< 0.1%
-7.370022847 1
< 0.1%
-7.369076955 1
< 0.1%
-7.293413502 1
< 0.1%
-7.114686207 1
< 0.1%
-7.074182167 1
< 0.1%
-7.048911727 1
< 0.1%
ValueCountFrequency (%)
117.3788564 1
< 0.1%
111.963234 1
< 0.1%
108.3094824 1
< 0.1%
108.1831096 1
< 0.1%
107.7032071 1
< 0.1%
105.4779893 1
< 0.1%
104.4486134 1
< 0.1%
103.3344671 1
< 0.1%
102.59429 1
< 0.1%
102.1703515 1
< 0.1%

native-country
Real number (ℝ)

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.8924
Minimum0
Maximum41
Zeros134
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-12-07T22:54:58.095957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q139
median39
Q339
95-th percentile39
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.1594656
Coefficient of variation (CV)0.19406343
Kurtosis14.339089
Mean36.8924
Median Absolute Deviation (MAD)0
Skewness-3.8107823
Sum368924
Variance51.257948
MonotonicityNot monotonic
2023-12-07T22:54:58.200187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
39 8882
88.8%
26 282
 
2.8%
0 134
 
1.3%
30 87
 
0.9%
8 32
 
0.3%
19 30
 
0.3%
35 27
 
0.3%
22 24
 
0.2%
33 22
 
0.2%
23 21
 
0.2%
Other values (32) 459
 
4.6%
ValueCountFrequency (%)
0 134
1.3%
1 14
 
0.1%
2 13
 
0.1%
3 21
 
0.2%
4 10
 
0.1%
5 20
 
0.2%
6 17
 
0.2%
7 18
 
0.2%
8 32
 
0.3%
9 7
 
0.1%
ValueCountFrequency (%)
41 16
 
0.2%
40 18
 
0.2%
39 8882
88.8%
38 19
 
0.2%
37 14
 
0.1%
36 14
 
0.1%
35 27
 
0.3%
34 15
 
0.1%
33 22
 
0.2%
32 12
 
0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
8349 
0
1651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Length

2023-12-07T22:54:58.294269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:54:58.380641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Most occurring characters

ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8349
83.5%
0 1651
 
16.5%

Interactions

2023-12-07T22:54:53.786162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.470518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.776565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.987741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.169422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.363420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.549922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.712296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.888867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.255371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.433107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.619370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.871746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.556114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.871501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.077096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.265469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.454844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.639820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.803779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.201139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.344277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.523365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.707561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.972412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.655878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.978933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.185289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.373996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.559347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.741529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.907783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.300941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.449927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.626739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.811788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.067396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.753119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.081388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.282153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.481300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.661557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.840976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.008403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.396153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.549501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.727808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.909944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.164796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.851390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.183526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.383835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.581074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.761873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.941484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.109609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.493425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.651554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.827846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.011291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.261827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:40.947991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.288784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.485008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.683114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.863743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.044006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.210228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.594030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.753511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.930755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.113115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.356310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.039740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.392478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.584316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.780746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.961748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.139483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.309277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.694433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.850480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.026975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.210719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.452771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.137497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.495298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.686281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.883106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.065765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.240138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.408715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.791447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.953670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.129551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.311938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.543768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.229376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.591886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.781525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.976383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.161421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.332076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.503424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.881483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.047368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.229430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.405052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.636970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.321919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.693411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.879853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.075286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.261142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.429789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.600936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:49.975509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.144703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.333581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.502679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.731041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.587480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.794688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:43.979871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.173509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.359931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.525902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.699616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.070491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.242636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.431340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.600609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:54.823802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:41.684329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:42.894118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:44.077851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:45.273124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:46.459142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:47.623750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:48.796942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:50.167267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:51.342019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:52.530295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:54:53.695651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-12-07T22:54:58.457810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipcapital-gaincapital-losshours-per-weeknative-countryracesextarget
age1.0000.0410.024-0.0030.0260.0260.0090.0020.0480.0100.014-0.0020.0170.0140.032
workclass0.0411.0000.006-0.0120.018-0.005-0.007-0.0070.0060.022-0.0220.0110.0150.0980.047
fnlwgt0.0240.0061.000-0.021-0.0000.035-0.0100.021-0.060-0.008-0.0500.0070.0330.0440.016
education-0.003-0.012-0.0211.000-0.008-0.0620.0170.037-0.013-0.032-0.0010.0150.0230.0640.055
education-num0.0260.018-0.000-0.0081.0000.0110.0120.0120.0010.0090.025-0.0110.0200.0550.069
marital-status0.026-0.0050.035-0.0620.0111.000-0.0270.0250.0140.027-0.027-0.0230.0200.1270.038
occupation0.009-0.007-0.0100.0170.012-0.0271.0000.003-0.0290.0200.0420.0010.0280.0390.066
relationship0.002-0.0070.0210.0370.0120.0250.0031.000-0.044-0.054-0.0000.0180.0140.1630.115
capital-gain0.0480.006-0.060-0.0130.0010.014-0.029-0.0441.000-0.109-0.0720.0020.0000.0000.028
capital-loss0.0100.022-0.008-0.0320.0090.0270.020-0.054-0.1091.000-0.1080.0070.0170.0190.022
hours-per-week0.014-0.022-0.050-0.0010.025-0.0270.042-0.000-0.072-0.1081.000-0.0010.0150.0000.000
native-country-0.0020.0110.0070.015-0.011-0.0230.0010.0180.0020.007-0.0011.0000.0030.0140.024
race0.0170.0150.0330.0230.0200.0200.0280.0140.0000.0170.0150.0031.0000.0340.000
sex0.0140.0980.0440.0640.0550.1270.0390.1630.0000.0190.0000.0140.0341.0000.081
target0.0320.0470.0160.0550.0690.0380.0660.1150.0280.0220.0000.0240.0000.0811.000

Missing values

2023-12-07T22:54:54.960031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-07T22:54:55.181410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
038.426234433512.993587159.02304119.712987-3.54911039.971673391
136.5407654369555.878603613.02504021.398018-5.87248117.70800781
224.1371024129336.7961451514.047540109578.056894-7.23128817.868293391
334.2647914146540.074892513.047341-43.838008-2.73645040.120083101
432.9776464242860.280789115.04304153.191204-1.33762758.353321391
518.3761344317972.3839891014.0012121-120.0798593.00947039.769978391
629.28835948985.1329151514.003011128.2260573.56370839.726562390
738.8678164198580.3775661512.02804017.900497-5.88580539.890537391
818.5616314111726.8027571510.023020-43.817705-3.26035740.251512391
982.6398606144589.336652154.0433402416.363291-4.14142944.958724391
ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
999033.5759885167157.261000119.006040-13.0339952.73021840.084302391
999129.7461730166779.623084910.024040120.193645-7.15416439.907779391
999219.7137974187234.20087196.041204048.521430-4.90293840.908768391
999331.5081904209595.238300514.04734180.091947-4.99695439.761219391
999445.3246714160843.48783539.058041104.893817-2.50586940.129714390
999536.0710504324293.554725114.044040-56.4313282.00114243.442892391
999626.1263150127335.6864971112.0261414417.6023471.40427339.889016351
999762.463071436884.4397501110.06004132.7748245.49180639.765348390
999842.7596736185867.2860461512.0210417187.084519-2.32454839.761384390
999943.8203476213495.958902159.02104071.918633-3.084349-3.606185391